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58th IEEE/IAS Industrial and Commercial Power Systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Cheng, Tengfei Li, Baohong Jiang, Qin Su, Xueneng Zhang, Jian Zhang, Hua Sichuan Univ Coll Elect Engn Chengdu Peoples R China State Grid Corp China Sichuan Elect Power Res Inst Chengdu Peoples R China State Grid Sichuan Elect Power Co Chengdu Peoples R China

ISBN: (数字)9781665450669

ISBN: (纸本)9781665450669

In order to overcome the limitations of traditional methods and single-feature identification of single-phase fault lines in distribution networks, this paper proposes a machine learning fault line selection method based on multi-features and Bayesian optimization. First, the characteristic changes of electrical quantities of the single-phase-to-ground fault in the distribution network are analyzed, and a feature engineering based on multiple steady-state and transient electrical quantities is constructed. Secondly, the principal component analysis (PCA) dimensionality reduction and synthetic minority over-sampling technique (smote) are introduced to solve the problem of high feature correlation and unbalanced sample categories. Furthermore, aiming at the problem of hyperparameter tuning existing in the classifier, a Bayesian optimization-based support vector machine(SVM) model is proposed, which realizes the automatic optimization of hyperparameters. Finally, the model is trained with the experimental recorded wave data, and the overall performance of the model is evaluated by the learning curve and ROC curve, which verifies the satisfied fault line identification ability of the model in the face of complex situations and different environmental configurations.

关键词： Multi-feature recognition Bayesian optimization SVM PCA dimensionality reduction smote algorithm

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Improving User's Quality of Experience in Imbalanced Dataset

Improving User's Quality of Experience in Imbalanced Dataset

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International Computer Symposium (ICS)

作者： Wang, Tanghui Huang, Ruocheng Wei, Xin Zhou, Fang Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China

ISBN: (纸本)9781509034383

Currently, traditional algorithm performs not well in terms of predicting the user's complaint in imbalanced IPTV dataset. To solve this problem, we combine status data from the set-top box with data of user's complaints and select the appropriate model to predict user's quality of experience (QoE). Concretely, we firstly perform data cleaning and select suitable attributes from the original dataset. Then, we apply random under-sampling and synthetic over-sampling to the preprocessed dataset. In order to get better performance, we improves the Synthetic Minority Over-sampling Technique (smote) algorithm and combine it with K-means algorithm to generate a new dataset. After these procedures, we use the Naive Bayes (NB) model in user's complaint dataset. Through the rigorous modeling and prediction, extensive experimental results show that this integrated algorithm performs better than the Borderline-smote algorithm in predicting user's complaints.

关键词： QoE smote algorithm K-means Naive Bayes model

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The Electricity Bill Charge Risk Analysis in Power Supply Company based on A Novel Predicting Method

The Electricity Bill Charge Risk Analysis in Power Supply Co...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

作者： Zhou, Gang Lv, Xinjie Yin, Wenjun Zhao, Wei Jin, Feng Dong, Jin IBM Res China Ind Solut E&U Logist & BAO Beijing 100193 Peoples R China

ISBN: (纸本)9781509043033

Risk Analysis of electricity bill charge has been both challenging and important in the field of electricity power supply in China. In this paper, a novel electricity bill charge risk predicting method is proposed. The smote (synthetic minority oversampling technique) algorithm is first used to under-sampling the majority class and over-sampling the minority class, and then it is combined with some state-of-the-art classification methods to predict the electricity charge risk based on an imbalanced data set obtained from a power supply enterprise. The results of the empirical analysis demonstrate that a combination of smote algorithm with Random Forest method achieves better classification performance under several criterions. Furthermore, five important variables are listed for power supply enterprises to take corresponding measures to avoid charge risk.

关键词： Power market Risk analysis of electricity bill charge smote algorithm Random Forest

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Customer Churn Pre‐identification Method Based on Feature Engineering and Stacking Strategy

World Scientific Research Journal

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World Scientific Research Journal 2022年第6期8卷 677-684页

作者： Yuansheng Song Deyi Gong Liangao Guo Wanran Bu

With the rapid development of economy, commercial activities become more and more frequent. The customer has become the position that the enterprise and commercial activity scramble for, and the direction of the customer also decides the direction of the business. In order to sustain profits, business companies and enterprises should capture and retain customers, identify the customers who will be lost in advance, and make timely adjustments to retain them reasonably. The existing prediction models for churn are relatively single or simple and inefficient in the face of high‐dimensional data. In this paper, a model fusion strategy based on customer churn pre‐identification method is proposed. XGBoost and LightGBM were used as the first layer models, and LogisticRegression was used as the second layer model to prevent overfitting, Stacking strategy was used to build the model in this paper. By comparing the commonly used XGBoost and LightGBM, this model is superior to these two single models.

关键词： Data mining Feature analysis Ensemble learning Prediction model smote algorithm

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Study on Influencing Factors of Childbearing-Aged Women’s Willingness to Have Multiple Children in China

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Sociology Study 2023年第2期13卷 63-74页

作者： Dandan Cheng Jiashan Tang Nanjing University of Posts and Telecommunications NanjingJiangsuChina

The fertility rate of China has shown an overall decline trend,so the fully understanding of the factors affecting China’s fertility intention has become the focus of the *** on CFPS2020 data,the study subjects were women of age between 20 and 49,who were born after the 1970s and were influenced by the family planning ***,smote-Catboost algorithm was used to construct a *** results show that:(1)For the women’s willingness to have multiple children,the smote-Catboost algorithm is more effective than the Catboost algorithm,and the classification accuracy is improved by 8 percentage points.(2)The factors influencing women’s willingness to have multiple children were ranked by social status,intergenerational care,education level and *** with high social status are more willing to have multiple children;All-day intergenerational care has a positive effect on women’s willingness to have multiple *** women’s willingness to have multiple children declines with the rise of education level but increases with age.(3)Combined with the CFPS2018 data,the influencing factors changed over *** 2018,the important factors related to women’s willingness to have multiple children were mainly related to economic,within which the income ranked locally is the most important *** 2020,the most important factor changed to be social status while economic became less important.

关键词： smote algorithm Catboost algorithm fertility intention to have multiple child influencing factors China

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The Electricity Bill Charge Risk Analysis in Power Supply Company based on A Novel Predicting Method

The Electricity Bill Charge Risk Analysis in Power Supply Co...

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IEEE Innovative Smart Grid Technologies - Asia

作者： Gang Zhou Xinjie Lv Wenjun Yin Wei Zhao Feng Jin Jin Dong Industry Solutions (E&U logistics and BAO) IBM Research - China

ISBN: (纸本)9781509052288

关键词： Power market Risk analysis of electricity bill charge smote algorithm Random Forest

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Application of Hybrid Sampling Method in the Prediction of Telecom Customer Churn

Application of Hybrid Sampling Method in the Prediction of T...

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2021年第四届算法、计算和人工智能国际会议

作者： Shuai Ye Guoen Xia Xianquan Zhang Xiangqing Li School of Computer Science and Engineering Guangxi Normal University School of Business Administration Guangxi University of Finance and Economics

In the telecommunications industry, there is a widespread problem of data imbalance. This problem seriously affects the prediction results, making it impossible for telecommunications operators to accurately find potential lost customers, causing a lot of *** at the problem of economic loss caused by the imbalance of telecommunications customer data that affects model prediction performance, this paper proposes two hybrid algorithms DB-QCS(DBSCAN Quadrilateral centroid smote) and KM-QCS(K-Means Quadrilateral centroid smote) to solve the above problems, The hybrid algorithm mainly solves the problem of further increasing the marginalization of the sample distribution and introducing noise when the smote algorithm synthesizes new samples. The main idea is to first use the under-sampling method to delete outliers or edge samples in most classes of samples, thereby reducing the number of synthesized new samples to solve the problem of introducing excessive noise. Then, the problem of marginalization of the sample distribution is solved by limiting the synthesis area of the new sample during oversampling, and finally the sampled data set is used for classification training. A large number of experiments on 5 unbalanced telecom customer data sets show that the hybrid algorithm achieves higher F-measure, G-mean and AUC values compared with the smote algorithm.

关键词： customer churn Unbalanced customers smote algorithm DBSCAN algorithm K-Means algorithm

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Multi-Classification of Rainfall Weather Based on Deep Learning-Mod

Multi-Classification of Rainfall Weather Based on Deep Learn...

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第三十九届中国控制会议

作者： Zhiying Lu Xudong Ding Yimo Ren Xiaolei Sun School of Electrical and Information Engineering Tianjin University

Rain is one of the common weather conditions in the meteorological field, and at the same time it will have an important impact on people's daily life, industry and agricultural development. Rainfall is related to physical parameters of weather conditions, such as temperature, saturated water pressure, water vapor density, temperature and humidity. This paper constructs a rainfall multi-classification model based on physical quantities and deep learning model DBNs. Firstly, to resolve the problem of uneven distribution of the samples of the original data, some rainfall samples were synthesized using the smote algorithm. Secondly, the observation data encrypted hourly by the automatic ground monitoring station and physical quantities commonly used in weather forecast analysis are used to construct a deep learning model containing a Gauss Boltzmann machine to extract the intrinsic characteristics of the original data. Finally, a multi-class identification model of DBNs rainfall weather was realized. Experiments show that compared with other methods, the model can more accurately identify rainfall weather, and the POD, FAR and CSI indicators all perform well.

关键词： Rainfall classification Physical quantity calculation smote algorithm Deep learning

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Application of Hybrid Sampling Method in the Prediction of Telecom Customer Churn 21

Application of Hybrid Sampling Method in the Prediction of T...

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Proceedings of the 2021 4th International Conference on algorithms, Computing and Artificial Intelligence

作者： Shuai Ye Guoen Xia Xianquan Zhang Xiangqing Li School of Computer Science and Engineering Guangxi Normal University China School of Business Administration Guangxi University of Finance and Economics China

ISBN: (纸本)9781450385053

In the telecommunications industry, there is a widespread problem of data imbalance. This problem seriously affects the prediction results, making it impossible for telecommunications operators to accurately find potential lost customers, causing a lot of losses. Aiming at the problem of economic loss caused by the imbalance of telecommunications customer data that affects model prediction performance, this paper proposes two hybrid algorithms DB-QCS (DBSCAN Quadrilateral centroid smote) and KM-QCS (K-Means Quadrilateral centroid smote) to solve the above problems, The hybrid algorithm mainly solves the problem of further increasing the marginalization of the sample distribution and introducing noise when the smote algorithm synthesizes new samples. The main idea is to first use the under-sampling method to delete outliers or edge samples in most classes of samples, thereby reducing the number of synthesized new samples to solve the problem of introducing excessive noise. Then, the problem of marginalization of the sample distribution is solved by limiting the synthesis area of the new sample during oversampling, and finally the sampled data set is used for classification training. A large number of experiments on 5 unbalanced telecom customer data sets show that the hybrid algorithm achieves higher F-measure, G-mean and AUC values compared with the smote algorithm.

关键词： K-Means algorithm Unbalanced customers smote algorithm DBSCAN algorithm customer churn

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Financial statements fraud identifiers

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Economic Research-Ekonomska Istrazivanja 2023年第3期36卷

作者： Zenzerović, Robert Šajrih, Josip Faculty of Economics and Tourism “Dr. Mijo Mirković” Juraj Dobrila University of Pula Pula Croatia Basellandschaftliche Kantonalbank Pratteln Switzerland

Contemporary research among fraud professionals indicates that organizations lose 5% of revenues from fraud every year which makes the research in this area and the derivation of fraud detection models very important. The purpose of the article is to develop a new accounting tool that will help companies and investors in prompt fraud detection and prevention which can finally result in the preservation of financial stability as well as more efficient capital allocation. In this context the main objective of the research is to test the significance of some financial statements positions’ relations that has not been used in the previous research using the dataset from SEC AAERs presented and included in Bao et al.’s research as well as to combine them with existing ones and consequently develop new financial statement fraud detection model. Another objective consists of presenting some of the most significant and contemporary research in the field of financial statement fraud detection models and comparing their quality using the ROC analysis. Research results were generated by using the smote algorithm and logistic regression analysis on the dataset of 146,045 cases for a period from 1982 to 2014 and point out five independent variables used by Bao et al. The financial statement fraud detection model comprised of change in free cash flow, percentage of soft assets, sale of common and preferred stock, change in cash sales, and change in receivables shows a sufficient level of discriminant power with 67% area under ROC curve. The model derived could be used as a starting point for fraud detection preventing the significant losses the company and stakeholders could face. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Financial statement fraud fraud detection models logistic regression analysis ROC analysis smote algorithm

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